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To develop an optimal operational scheme for distribution networks capable of addressing asymmetric uncertainties associated with renewable energy and load demands, this paper presents a confidence level-based information gap decision theory (CL-IGDT) framework. Building on IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To facilitate such probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties. Consequently, a two-stage robust optimal operation model for distribution networks using CL-IGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing methods, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%.
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To develop an optimal operational scheme for distribution networks capable of addressing asymmetric uncertainties associated with renewable energy and load demands, this paper presents a confidence level-based information gap decision theory (CL-IGDT) framework. Building on IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To facilitate such probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties. Consequently, a two-stage robust optimal operation model for distribution networks using CL-IGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing methods, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%.
Accurate fault location in multiterminal DC (MTDC) systems is hindered by topological ambiguity and measurement synchronization uncertainties. This article presents a convex optimization-based fault location framework characterized by low computational burden and global optimality. By introducing a continuous perspective relaxation strategy, the nonconvex combinatorial search problem is reformulated into a strictly convex rotated second-order cone programming (RSOCP) model. This guarantees global optimality, overcoming the vulnerability of traditional analytical methods to synchronization errors, while bypassing the local minima traps of heuristic algorithms and the extrapolation vulnerability of data-driven models. To enhance robustness, a Cauchy M-estimator-based iteratively reweighted least squares mechanism is integrated, allowing the framework to autonomously suppress synchronization noise and extreme measurement deviations. The RSOCP model is solved via the primal-dual interior-point method. Dynamic evaluations on four-terminal and 13-terminal meshed MTDC systems verify the framework’s accuracy and reliability even under low sampling frequencies, high fault resistances, and adverse noisy conditions.
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Accurate fault location in multiterminal DC (MTDC) systems is hindered by topological ambiguity and measurement synchronization uncertainties. This article presents a convex optimization-based fault location framework characterized by low computational burden and global optimality. By introducing a continuous perspective relaxation strategy, the nonconvex combinatorial search problem is reformulated into a strictly convex rotated second-order cone programming (RSOCP) model. This guarantees global optimality, overcoming the vulnerability of traditional analytical methods to synchronization errors, while bypassing the local minima traps of heuristic algorithms and the extrapolation vulnerability of data-driven models. To enhance robustness, a Cauchy M-estimator-based iteratively reweighted least squares mechanism is integrated, allowing the framework to autonomously suppress synchronization noise and extreme measurement deviations. The RSOCP model is solved via the primal-dual interior-point method. Dynamic evaluations on four-terminal and 13-terminal meshed MTDC systems verify the framework’s accuracy and reliability even under low sampling frequencies, high fault resistances, and adverse noisy conditions.
